Efficient channel characteristics handling

ABSTRACT

A method, performed in a network node, for channel characteristics handling for an antenna array in a communication system. The antenna array has a plurality of antenna elements. The method includes obtaining geometrical relationships between any pair of antenna elements in a spatial layout of the antenna array. All pairs of antenna elements are classified into sets based on the obtained geometrical relationships, wherein all pairs of antenna elements in a set have substantially equal geometrical relationship in the spatial layout. The method includes determining a representation of channel characteristics as P(β), wherein argument β is a vector of elements, each element relating to a magnitude and/or phase of covariance between the antenna elements in the set, and P is a mapping function based on the classifying. Antenna characteristics are processed based on the representation P(β).

TECHNICAL FIELD

The present disclosure relates to methods and arrangements for improvedchannel characteristics handling, in particular methods and arrangementsfor handling channel characteristics for large antenna arrays.

BACKGROUND

The 3rd Generation Partnership Project, 3GPP, is responsible for thestandardization of the Universal Mobile Telecommunication System, UMTS,and Long Term Evolution, LTE. The 3GPP work on LTE is also referred toas Evolved Universal Terrestrial Access Network, E-UTRAN. LTE is atechnology for realizing high-speed packet-based communication that canreach high data rates both in the downlink and in the uplink, and isthought of as a next generation mobile communication system relative toUMTS. LTE brings significant improvements in capacity and performanceover previous radio access technologies. However, while operators' havebeen successful in providing mobile broadband using LTE deployments,user experience in terms of latency and data rates is a differentiatingfactor between different operators' networks. The ever increasingend-user demands are a significant challenge to the operators.

The modernization of antenna technologies in practice is moving forwardin a high pace, which enables the use of more advanced antenna setupsand techniques. Large antenna arrays have been used in radarapplications, satellite communications, and point-to-pointcommunications. The possible use of large antenna arrays for wirelesscellular communications is also being considered in order to increasecapacity and performance in a mobile radio network. The use of multipleantennas combined with adequate processing is one way to improve thespectral efficiency of a communication system.

When using multiple antennas, e.g., in the form of large antenna arraysat base stations in a mobile radio network, transmissions between nodesof the radio network, such as between said base stations and wirelessdevices, pass over several antenna elements. Therefore, a radio channelbetween, e.g., a wireless device and a serving base station having aplurality of antenna elements is multi-dimensional in that there is aplurality of propagation paths between the wireless device and thedifferent antenna elements of the serving base station, where each pathis associated with a gain and a phase, relative to the other paths or toa reference value. It is important for many reasons to be able tocharacterize this multi-dimensional channel.

A traditional way of characterizing this type of multi-dimensionalchannel is by a so-called covariance matrix of the channel, whichprovides information on a stochastic relationship between signalspassing over the different antenna elements. This covariance matrix isoften signalled between nodes of the network and also used in signalprocessing in the communication system, e.g., in beamformingapplications.

However, the size of the above-mentioned covariance matrix grows withthe square of the number of elements in the antenna array. Large antennaarray sizes may hence tax resources concerning computational power andmemory storage, even for simple sample covariance estimators.Furthermore, in cases where the channel state information, CSI, that ispassed e.g. between nodes in a communication network includes covariancematrix information, the transmission bandwidth of the inter-node linkmay be overloaded. While this may be manageable for a single link or formoderately-sized arrays, the size of the matrix in combination with alarge number of concurrent communication links may become unmanageable.

There is thus a need in the art for improved handling of channelcharacteristics.

SUMMARY

An object of the present disclosure is to provide methods andarrangements for improved handling of channel characteristics for anantenna array in a communication system, the antenna array having aplurality of antenna elements.

This object is achieved by a method, performed in a network node, forchannel characteristics handling for an antenna array in a communicationsystem, the antenna array having a plurality of antenna elements. Themethod comprises obtaining geometrical relationships between any pair ofantenna elements in a spatial layout of the antenna array. The methodfurther comprises a step of classifying all pairs of antenna elementsinto sets based on the obtained geometrical relationships, wherein allpairs of antenna elements in a set have substantially equal geometricalrelationship in the spatial layout. The method also comprisesdetermining a representation of channel characteristics as P(β), whereinargument β is a vector of elements, each element relating to a magnitudeand/or phase of covariance between the antenna elements in the set, andP is a mapping function based on the step of classifying. The methodfurther comprises processing antenna characteristics based on therepresentation P(β).

An advantage of this solution is that estimation of channelcharacteristics in the form of channel covariance can be performed withsimilar quality, but lower complexity than prior art methods. Anotheradvantage is that Channel State Information, CSI, reporting, and storageof CSI reports, can be made more efficient. A further advantage is thatthe traffic load of inter-node links is reduced. A yet further advantageis that memory requirements are reduced when it comes to storing channelcharacteristics. Another advantage is that signal processing operationsrelating to channel characteristics can be made more efficient in termsof implementation.

According to an aspect of the disclosure, the step of processing antennacharacteristics comprises performing a measurement on at least onesignal and estimating elements of β based on the measurement.

By estimating elements of β based on the measurement, the quality of thechannel characteristics estimation is improved. An estimation of achannel covariance will, for a given number of data points and a givensignal to noise ratio, SNR, be of better quality the fewer uniqueparameters need to be estimated. This is ensured by the choice ofrepresentation P(β).

According to an aspect of the disclosure, the step of performing ameasurement on at least one signal is based on a processing of at leastone signal representing respective at least one pair of a set obtainedin the classifying.

An advantage of this is that the determination of β can be optimisedwith respect to system performance, size of the classified sets orproperties of the geometrical relationships of the classified sets.

According to an aspect of the invention, the step of processing antennacharacteristics comprises transmitting information relating to β.

According to an aspect, the step of transmitting information relating toβ comprises transmitting β between at least two network nodes.

According to an aspect, the step of transmitting information relating toβ comprises transmitting β to a memory.

The performance and device demands of operations involving transmittinginformation relating to channel characteristics, such as a covariancematrix, can be improved by transmitting information relating to βinstead of transmitting, e.g., a covariance matrix as a regular list ofcolumns or rows of the matrix.

According to an aspect, the step of processing antenna characteristicscomprises performing a signal processing operation involving thecovariance between antenna elements, the signal processing operationbeing expressed as a function of the elements of β.

Expressing the signal processing operation as a function of the elementsof β reduces the complexity of the signal processing operation.

According to an aspect, the step of performing a signal processingoperation involving the covariance between antenna elements comprisesdetermining at least one weight vector w based on the elements of β, theweight vector w consisting of antenna weights being used to adjustmagnitude and phase of signals to and from antenna elements of theantenna array.

Consequently, beamforming is made more efficient. According to anaspect, the step of processing antenna characteristics comprisestransmitting information relating to P, the information relating to Penabling estimation of β based on a measured signal.

According to an aspect, the information relating to P comprises therepresentation P(β).

According to an aspect, the information relating to P comprises anindication relating to a representation P(β) stored at a receiving node.

This has the advantage of sharing the representation between nodes.

According to an aspect, the step of determining a representationcomprises an error tolerance, such that

P(β)≈R

within the error tolerance, wherein ≈ denotes approximately equal to,and covariance between antenna elements is represented by a covariancematrix R.

The use of an error tolerance enables taking into account variations ofthe spatial layout of the antenna array, in particular when obtaininggeometrical relationships and classifying pairs of antenna elements.

According to an aspect, P is a matrix defined by

vec{R_(mn)}=Pβ

wherein covariance between antenna elements is represented a covariancematrix R_(mn), vec{R_(mn)} denoting the vectorization of R_(mn), m and nbeing the number of antenna elements of the antenna array.

This representation enables a complexity reduction of many operationsinvolving a covariance matrix.

According to an aspect, the elements of β are real-valued.

This simplifies the implementation of the representation P(β). By βhaving only real-valued elements, more information of the representationP(β) is related to P. Since β is the part of the representation that istypically transferred most frequently between network nodes, the networkload is reduced. Furthermore, the elements of β are used in many signalprocessing operations. The elements of β being real-valued enable acomplexity reduction of many of those signal processing operations.

The present disclosure also relates to a network node and a computerprogram that implement the disclosed method, with the advantagesdescribed above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1a illustrates an aspect of the disclosed method for a base stationin a beamforming application.

FIG. 1b illustrates an aspect of the disclosed method for a radar systemin a beamforming application.

FIG. 2 shows a flowchart illustrating method steps performed in anetwork node.

FIG. 3 shows a block diagram illustrating optional features inprocessing antenna characteristics.

FIG. 4 illustrates antenna arrays and geometrical relationships betweenantenna elements of the antenna arrays.

FIG. 5 illustrates an aspect of the method for a uniform linear arraycomprising four antenna elements.

FIG. 6 illustrates aspects of the method used in uplink and downlink ina communication network.

FIG. 7 illustrates an aspect of error tolerance when determining arepresentation.

FIG. 8 illustrates an aspect of the method used for radar.

FIG. 9 illustrates an aspect of the method for a uniform linear arraycomprising four antenna elements, in analogy with FIG. 5, but whereinthe representation comprises fewer unique elements.

FIG. 10 illustrates an aspect of the method for a uniform linear arraycomprising four antenna elements, in analogy with FIG. 5, but whereinthe representation comprises more unique elements.

FIG. 11 illustrates an aspect of the method for two base stations.

FIG. 12 illustrates a network node arranged to handle channelcharacteristics for an antenna array according to an aspect of thedisclosure.

FIG. 13 illustrates modules of a terminal for handling channelcharacteristics for an antenna array in a communication system,according to an aspect of the disclosure.

FIG. 14 illustrates methods for handling channel characteristics for anantenna array in a communication system.

FIG. 15 illustrates modules of a wireless device for handling channelcharacteristics for an antenna array in a communication system.

DETAILED DESCRIPTION

Aspects of the present disclosure will be described more fullyhereinafter with reference to the accompanying drawings. The methods andarrangements disclosed herein can, however, be realized in manydifferent forms and should not be construed as being limited to theaspects set forth herein. Like numbers in the drawings refer to likeelements throughout.

The terminology used herein is for the purpose of describing particularaspects of the disclosure only, and is not intended to limit thedisclosure. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise.

FIG. 1a illustrates an aspect of the disclosed method for a base stationin a beamforming application. The base station comprises an antennaarray A1, wherein the antenna array A1 comprises a plurality of antennaelements. The base station uses a representation P(β) of channelcharacteristics according to an aspect of the present disclosure. Thebase station transmits information relating to P to a wireless deviceO1, so that the wireless device O1 can perform measurements on thesignals received in downlink and estimating β based on the measurements.β is then transmitted from the wireless device O1 to the base station inuplink. The base station uses β in a signal processing operation,wherein the effects on signal gain relating to a set of weight vectorsare evaluated based on p. The weight vector predicting the best signalgain is used to process the antenna characteristics B1 by means ofbeamforming.

A communication link is associated with a certain capacity in terms of,e.g. bits/sec. By the proposed technique of representing channelcharacteristics more efficiently, less of this capacity is taken up bytransmissions of channel characteristics, leaving more room for payloadtransmissions, i.e. user data. The signal processing operations usingthe representation according to this aspect are reduced in complexity,leading to faster processing times and reduced load on memory andbandwidth.

FIG. 1b illustrates an aspect of the disclosed method for a radar systemin a beamforming application. The radar system comprises an antennaarray A2, wherein the antenna array A2 comprises a plurality of antennaelements. The radar transmits signals that are reflected on an object O2and the reflected signals are received by the antenna array A2, whereinantenna characteristics is handled according to an aspect of the presentdisclosure, the aspect comprising a representation P(β) of channelcharacteristics, wherein argument β is a vector of elements and P is amapping function. The radar performs a measurement on the receivedsignals and uses the measurement to determine elements of the vectorargument β. According to an aspect, the effects on signal gain of aplurality of weight vectors w, wherein each weight vector consisting ofantenna weights being used to adjust magnitude and/or phase of signalsto and from antenna elements of the antenna array, are determined.According to a further aspect, the signal gain is a function of theelements of β and the weight vectors. The weight vector predicting thebest signal gain is used to process the antenna characteristics B2 bymeans of beamforming.

FIG. 2 shows a flowchart illustrating method steps for channelcharacteristics handling for an antenna array in a communication system,wherein the antenna array has a plurality of antenna elements and themethod is performed in a network node.

The method comprises obtaining S1 geometrical relationships between anypair of antenna elements in a spatial layout of the antenna array.According to an aspect, the geometrical relationship between eachantenna pair is represented by a vector indicating the relative positionof one antenna element in a pair with respect to the other antennaelement of the same pair.

The method further comprises classifying S3 all pairs of antennaelements into sets based on the obtained geometrical relationships,wherein all pairs of antenna elements in a set have substantially equal,or equal, geometrical relationship in the spatial layout. According toan aspect, all pairs of antenna elements having the same relativeposition with respect to each other in the spatial layout of the antennaarray, up to a sign or direction of the position indicator, areclassified to belong to the same set. The term “substantially” is to beunderstood as that we do not require the geometrical relationships to beexactly equal, but close enough. According to an aspect, substantiallyequal depends on a measure relating to signal wavelength. A furtheraspect illustrating substantially equal is illustrated in FIG. 7.

The method also comprises determining S5 a representation of channelcharacteristics as P(β), wherein argument β is a vector of elements,each element relating to a magnitude and/or phase of covariance betweenthe antenna elements in the set, and P is a mapping function based onthe classifying S3 According to an aspect, the representation P(β)approximates a covariance matrix R as vec{R}=Pβ, wherein vec{R} denotesvectorization of R, P is a matrix and the elements of β are real valued.

The method additionally comprises processing S7 antenna characteristicsbased on said representation P(β). According to one aspect of thedisclosure, β is transferred between network nodes in relation toconveying Channel State Information. According to a further aspect ofthe disclosure, a signal processing operation is performed, wherein thesignal processing operation is a function of elements of β. In a yetfurther aspect of the disclosure, the signal processing operation ispart of a beamforming operation.

Embodiments of the disclosed method are not limited only to singlenetwork nodes, but may be implemented for handling channelcharacteristics for a plurality of nodes. Aspects comprising two basestations, each base station comprising an antenna array with a pluralityof antenna elements, are illustrated in FIG. 11 below. Embodiments ofthe disclosed method may also handle channel characteristics forwireless devices and base stations, wherein both a wireless device and abase station each comprises an antenna array, wherein each antenna arraycomprises a plurality of antenna elements. According to an aspect of thedisclosed method, channel characteristics between a wireless device anda base station, each comprising an antenna element having a plurality ofantenna elements, is handled, wherein handling the channelcharacteristics comprises determining S5 a representation P(β) of a3D-covariance matrix. According to another aspect of the disclosedmethod, handling channel characteristics comprises handling lists ofcovariance matrices, one for each transmission antenna element.

FIG. 3 shows a block diagram illustrating optional features inprocessing S7 antenna characteristics. According to one aspect of thedisclosure, processing S7 antenna characteristics comprises performingS71 a measurement on at least one signal and estimating S72 elements ofβ based on the measurement. Aspects of performing S71 a measurement onat least one signal and estimating S72 elements of β based on themeasurement are illustrated further in relation to FIG. 6.

According to an aspect of the disclosure, the step of performing S71 ameasurement on at least one signal is based on a processing S73 of atleast one signal representing respective at least one pair of a setobtained in the classifying S3 The sets of antenna pairs obtained in theclassifying S3 typically comprise a plurality of geometries. Eachantenna pair of such a set is represented by a signal. According to someaspects, as will be described in more detail below, the antenna pairs ofa set obtained in the classifying S3 are represented by a representativepair of antenna elements.

According to a further aspect of the disclosure, the step of performingS71 a measurement on at least one of the received signals is based on asignal representing a set obtained in the classifying S3 Here, therepresentative pair is selected to be one of the antenna pairs of theset obtained in the classifying S3 and the signal representing the setrelates to a signal representing the selected antenna pair. In terms ofcomplexity, this is one of the simplest ways of estimating β, since onlysignals relating to one antenna pair of a respective set obtained in theclassifying S3 is used.

According to another aspect of the disclosure, the step of performingS71 a measurement on at least one of the received signals is based on aprocessing S73 of a plurality of signals representing respectiveplurality of pairs of a set obtained in the classifying S3 For thisaspect, the representative antenna pair of a set obtained in theclassifying is based on a plurality of the antenna pairs of the set.This enables embodiments where one optimizes the estimation of β withrespect to complexity, i.e. how many of the antenna pairs within a setobtained in the classifying that is used as a basis for determining therepresentative antenna pair of the set, and thus the signal relating tothe representative antenna pair which is used to estimate β.

According to an aspect, the processing of a plurality of signalscomprises an averaging of the plurality of signals. According to afurther aspect, the processing of a plurality of signals comprises aweighted averaging of the plurality of signals. This enables embodimentswhere one optimizes the estimation of β with respect to quality.

According to an aspect of the disclosure, processing S7 antennacharacteristics comprises transmitting S74 information relating to β.According to an aspect of the disclosure, processing S7 antennacharacteristics comprises transmitting S74 information relating to βbetween at least two network nodes. According to an aspect of thedisclosure, processing S7 antenna characteristics comprises transmittingS74 information relating to β to a memory.

According to an aspect of the disclosure, processing S7 antennacharacteristics comprises performing S75 a signal processing operationinvolving covariance between antenna elements, wherein the signalprocessing operation is expressed as a function of the elements of β.According to an aspect of the disclosure, performing S75 a signalprocessing operation involving the covariance between antenna elementscomprises determining S76 at least one weight vector w based on theelements of β, the weight vector w consisting of antenna weights beingused to adjust magnitude and/or phase of signals to and from antennaelements of the antenna array.

According to an aspect of the disclosure, processing S7 antennacharacteristics comprises transmitting S77 information relating to P,the information relating to P enabling estimation of β based on ameasured signal. According to a further aspect, the information relatingto P comprises P. According to another aspect, the information relatingto P comprises an indication relating to a representation P(β) stored ata receiving node.

FIG. 4 illustrates antenna arrays and geometrical relationships betweenantenna elements of the antenna arrays.

Each antenna array comprises a set of antenna elements, wherein theantenna elements are represented by black dots, and geometricalrelationships between the antenna elements, wherein the geometricalrelationships are represented by arrows. FIGS. 4a ′-h′ illustrate therespective antenna arrays of FIGS. 4a-h , and further geometricalrelationships between the antenna elements, wherein the geometricalrelationships are represented by arrows.

Specifically, FIGS. 4a-h and 4a′-h′ illustrate

-   -   (a, a′) an antenna array comprising one antenna element    -   (b, b′) a uniform linear array comprising two antenna elements    -   (c, c′) a uniform linear array comprising three antenna elements    -   (d, d′) a non-uniform antenna array comprising three antenna        elements    -   (e, e′) a uniform linear array comprising four antenna elements    -   (f, f′) a 2×2 uniform planar array    -   (g, g′) a non-uniform antenna array comprising four antenna        elements    -   (h, h′) a 3×3 uniform planar array.

According to an aspect, the geometrical relationships are obtained S1by, for each pair of antenna elements in the antenna array, relating thedistance and direction of one antenna element of a pair to that of theother antenna element of the same pair. A pair of antenna elements is tobe understood to also comprise so-called auto-pairs, meaning thegeometrical relationship of a single antenna with itself.

According to an aspect, the respective positions of the antenna elementsm and n of any pair of antenna elements in a spatial layout of theantenna array are described by position vectors r_(m) and r_(n),respectively. According to a further aspect, a geometrical relationshipd_(mn) between the antenna elements of the pair of antenna elements isdefined by r_(m)-r_(n), as illustrated in FIGS. 4b and 4b ′. Thegeometrical relationship d_(mn)=r_(m)-r_(n), describing how one antennaelement is displaced with respect to another is hereinafter referred toas a displacement vector.

For an auto-pair, elements m and n refer to the same antenna element,r_(m) and r_(n) are identical and a displacement vectord_(mn)=r_(m)-r_(n) is a null vector, denoted {right arrow over (0)}.

When elements m and n refer to different antenna elements, r_(m) andr_(n) are different, and displacement vectors d_(mn) and d_(nm) arevectors of the same magnitude, but with opposite directions.

FIGS. 4a -4h illustrate displacement vectors d_(mn) for the respectiveantenna array and FIGS. 4a ′-4 h′ illustrate displacement vectorsd_(nm), i.e. of the same magnitude, but with opposite directionscompared to the vectors d_(mn). The displacement vectors that are nullvectors are not illustrated in FIGS. 4h and 4h ′ for clarity.

The method further comprises classifying S3 all pairs of antennaelements into sets based on the obtained geometrical relationships,wherein all pairs of antenna elements in a set have substantially equalgeometrical relationship in the spatial layout.

If the antennas have substantially identical radiation patterns and thearray is located in the far field of the sources or scatterers in thewireless channel, then the covariance or correlation R_(mn) between anypair of antenna elements m, n will only depend on the displacementvector d_(mn)=r_(m)-r_(n) between the two antenna elements, i.e.R_(mn)=f(d_(mn)), but not the individual position vectors r_(m) andr_(n) themselves, wherein f(d_(mn)) denotes a function of d_(mn).Further, the correlation or covarianceR_(mn)=f(d_(mn))=conj(f(−d_(mn)))=conj(R_(mn)), i.e., changing thedirection of the displacement vector results in a conjugate of thecovariance coefficient.

According to an aspect, all pairs of antenna elements having the samerelative position with respect to each other in the spatial layout ofthe antenna array, up to a sign or direction of the displacement vector,are classified to belong to the same set.

According to an aspect, pairs of antenna elements having geometricalrelationships differing only by a sign are classified S3 to belong tothe same set.

FIGS. 4a ″-4 h″ illustrate displacement vectors that represent anelement of a respective set, in which identical displacement vectors areclassified S3 to be substantially equal, up to a sign or direction ofthe displacement vector.

FIG. 5 illustrates an aspect of the method for a uniform linear arraycomprising four antenna elements.

FIG. 5a illustrates a uniform linear array comprising four antennaelements, wherein the antenna elements are represented by black dots,labelled 1, 2, 3 and 4. According to an aspect, geometricalrelationships between all pairs of antenna elements in a spatial layoutof the antenna array are obtained Si. The geometrical relationships areobtained S1 using displacement vectors d_(mn)=r_(m)-r_(n), where r_(m)and r_(n) are position vectors of the respective antenna element m, n ofa pair of antennas, as is illustrated in FIGS. 5b and 5c .

According to a further aspect, all pairs of antenna elements areclassified S3 into sets based on the obtained geometrical relationships,wherein all pairs of antenna elements in a set have substantially equalgeometrical relationship in the spatial layout. Pairs of antennaelements in a set having substantially equal geometrical relationshipsis based on the displacement vectors, wherein two pairs are consideredhaving substantially equal geometrical relationships if they arerepresented by substantially equal displacement vectors, up to a sign.As has been discussed in relation to FIGS. 2 and, the term“substantially” is to be understood as that we do not require thegeometrical relationships to be exactly equal, but only to within a normor measure relating to a metric.

FIGS. 5d-g illustrate sets comprising subsets of the geometricalrelationships of FIGS. 5b and c , wherein each set comprises geometricalrelationships that have been classified S3 to have substantially equalgeometrical relationship in the spatial layout, up to a sign.

The disclosed method comprises determining S5 a representation ofchannel characteristics as P(β), wherein argument β is a vector ofelements, each element relating to a magnitude and/or phase ofcovariance between the antenna elements in the set, and P is a mappingfunction based on the classifying S3.

According to an aspect, representative geometrical relationships of thesets defined in the classifying S3 are used as a basis for representingchannel covariance.

FIG. 5h illustrate representative geometrical relationshipscorresponding to a respective set of FIGS. 5d-5g . According to anaspect, each representative geometrical relationship is an element of arespective set of FIGS. 5d -5 g.

FIG. 5i illustrates a set comprising the unique representativegeometrical relationships of FIG. 5h . According to an aspect, theelements of the set of FIG. 5i are used as a basis for representingchannel covariance.

A detailed aspect is described below in relation to FIG. 5j-5i for anantenna as illustrated in FIG. 5a , using the obtained S1 geometricalrelationships, as illustrated in FIGS. 5b and 5c , and theclassification S3 of the obtained S1 geometrical relationships, asillustrated in FIGS. 5d -5 g.

According to an aspect, P is a matrix defined by

vec{R_(mn)}=Pβ  (1)

wherein covariance between antenna elements is represented by acovariance matrix R_(mn), vec{R_(mn)} denoting the vectorization ofR_(mn), m and n being indices running over the number of antennaelements of the antenna array.

FIG. 5j illustrates a representation of channel covariance, therepresentation comprising the elements of the set of FIG. 5i .

FIG. 5k illustrates a parametrisation of the representative geometricalrelationships of FIG. 5i . According to an aspect, the four displacementvectors of the set illustrated in FIG. 5k are represented by the realvalued parameters a, b, c, d, e, f and g, such that the null vector isrepresented by a and the others are represented by b-ic, d-ie, f-ig,respectively, where i is the imaginary unit, such that i²=−1.

FIG. 5l illustrates a covariance matrix based on the parametrisationillustrated in FIG. 5k of the representative geometrical relationshipsof FIG. 5i and the representation of channel covariance illustrated inFIG. 5j .

According to this aspect, as illustrated in FIG. 5l , the covariancematrix R_(mn) is represented by

$\begin{matrix}{R_{mn} = {\begin{matrix}1 \\2 \\3 \\4\end{matrix}\overset{\begin{matrix}1 & 2 & 3 & 4\end{matrix}}{\begin{bmatrix}a & {b - {ic}} & {d - {ie}} & {f - {ig}} \\{b + {ic}} & a & {b - {ic}} & {d - {ie}} \\{d + {ie}} & {b + {ic}} & a & {b - {ic}} \\{f + {ig}} & {d + {ie}} & {b + {ic}} & a\end{bmatrix}}}} & (2)\end{matrix}$

wherein the numbers 1-4 indicating the respective antenna element, asnumbered in FIG. 5a , have been added for clarity. Then, using

$\begin{matrix}{{{vec}\left\{ R_{mn} \right\}} = \begin{bmatrix}a \\{b + {ic}} \\{d + {ie}} \\{f + {ig}} \\{b - {ic}} \\a \\{b + {ic}} \\{d + {ie}} \\{d - {ie}} \\{b - {ic}} \\a \\{b + {ic}} \\{f - {ig}} \\{d - {ie}} \\{b - {ic}} \\a\end{bmatrix}} & (3)\end{matrix}$

P and β as determined by equation (1) above, are, according to anaspect, defined by

$\begin{matrix}{P = {\begin{bmatrix}1 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 1 & i & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 1 & i & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 1 & i \\0 & 1 & {- i} & 0 & 0 & 0 & 0 \\1 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 1 & i & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 1 & i & 0 & 0 \\0 & 0 & 0 & 1 & {- i} & 0 & 0 \\0 & 1 & {- i} & 0 & 0 & 0 & 0 \\1 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 1 & i & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 1 & {- i} \\0 & 0 & 0 & 1 & {- i} & 0 & 0 \\0 & 1 & {- i} & 0 & 0 & 0 & 0 \\1 & 0 & 0 & 0 & 0 & 0 & 0\end{bmatrix}\mspace{14mu} {and}}} & (4) \\{\beta = {\begin{bmatrix}a \\b \\c \\d \\e \\f \\g\end{bmatrix}.}} & (5)\end{matrix}$

Since P is based on the geometrical relationships, P is based on thestructure of the antenna array.

For an M×N uniform planar antenna array, M and N being the number ofantenna elements in each dimension, a corresponding covariance matrixholds (M*N)̂2 elements. The indices m and n of equation (1) would thusrun over (M*N) indices, respectively. A representation, such as therepresentation disclosed in relation to FIG. 5, generalised to an MxNuniform planar antenna array, has 2*M*N-M-N+1 unique elements that needsto be determined. For an antenna array where M and N are large, thecomplexity reduction approaches M*N/2.

5G is the next step of evolution in mobile communication. One of themain aims of 5G is to provide ubiquitous connectivity for any kind ofdevice and any kind of application that may benefit from beingconnected. While mobile broadband will continue to be important and willdrive the need for higher system capacity and higher data rates, 5G willalso provide wireless connectivity for a wide range of new applicationsand use cases, including wearables, smart homes, traffic safety/control,and critical infrastructure and industry applications, as well as forvery-high-speed media delivery. The use of large antenna arrays isbelieved to become an integral part of 5G.

According to an aspect for a 20×10 antenna array, which is in the rangeof what is studied for 5G evolution, this means that the number ofunique elements that needs to be determined can be reduced by about afactor of 100. Such a representation, when used for CSI reporting andstorage of CSI reports, can potentially improve performance ofcompression by about a factor of M*N/2 for an M×N uniform planar antennaarray. In addition to beamforming applications, the disclosed method canalso significantly improve performance in other applications involvinglarge antenna arrays, such as coordinated multipoint transmission, CoMP,and massive multiple-input multiple-output, MIMO.

FIG. 6 illustrates aspects of the method used in uplink and downlink ina communication network. FIG. 6a illustrates an aspect of processing S7antenna characteristics based on a representation P(β) in uplink, whileFIGS. 6b-6e relate to aspects of processing S7 antenna characteristics,based on a representation P(β), in downlink.

FIGS. 6a and 6e illustrate an aspect where a base station performsmeasurements on signals from a wireless device, FIG. 6a , and thenestimates β based on the measurements, and then uses the estimated β ina beamforming application, FIG. 6e .

FIGS. 6c-6e illustrate an aspect where a wireless device performsmeasurements on signals from a base station received in downlink, FIG.6c , then estimates β based on the measurements and transmits theestimated β to the base station, FIG. 6d , wherein the base station usesthe estimated β in a beamforming application, FIG. 6e .

These aspects are described in further detail below.

FIG. 6a illustrates an aspect of the disclosed method used in uplink ina communication network comprising a base station and a wireless device.The base station comprises an antenna array, wherein the antenna arrayhas a plurality of antenna elements. According to an aspect, geometricalrelationships between all pairs of antenna elements in a spatial layoutof the antenna array have been obtained S1 during manufacturing of theantenna array. All pairs of antenna elements have been classified S3into sets based on the obtained geometrical relationships, wherein allpairs of antenna elements in a set have substantially equal geometricalrelationship in the spatial layout. According to an aspect, theclassification S3 is done during a step of configuration of the antennaarray, prior to operational use of the antenna array. A representationof channel characteristics as P(β) has been determined S5, whereinargument β is a vector of elements, each element relating to a magnitudeand/or phase of covariance between the antenna elements in the set, andP is a mapping function based on the classifying S3 According to anaspect, the determination S5 of the representation P(β) is done during astep of configuration of the antenna array, prior to operational use ofthe antenna array. According to another aspect, P is determined at thebase station based on uplink measurements. According to yet anotheraspect, P is determined at the base station based on reporting from thewireless device.

In uplink, the wireless device signals the base station. Processing S7antenna characteristics based on said representation P(β) is done at thebase station using the received signals. According to an aspect of thedisclosure, a measurement is performed S71 at the base station on atleast one of the received signals and elements of β are estimated S72based on the measurement. According to a further aspect of thedisclosure, the step of performing S71 a measurement on at least one ofthe received signals is based on a signal representing a set obtained inthe classifying S3 In another aspect of the disclosure, the step ofperforming S71 a measurement on at least one of the received signals isbased on a processing S73 of a plurality of signals representingrespective plurality of pairs of a set obtained in the classifying S3According to an aspect, the processing of a plurality of signalscomprises an averaging of the plurality of signals. According to afurther aspect, the processing of a plurality of signals comprises aweighted averaging of the plurality of signals.

According to an aspect, the step of processing S7 comprises performingS75 a signal processing operation involving the covariance betweenantenna elements, the signal processing operation being expressed as afunction of the elements of β. According to a further aspect, the stepof performing S75 a signal processing operation comprises determiningS76 at least one weight vector w based on the elements of β, the weightvector w consisting of antenna weights being used to adjust magnitudeand/or phase of signals to and from antenna elements of the antennaarray. The channel estimates are thus used to combine the signalsreceived by different antennas in a form of receiver beamforming. Ifsignals from multiple antennas are received, and the combination is doneto match the channel for a given user and different users typically havedifferent channels, the received signal to noise and interference ratio,SINR, often increases significantly which in turn can often improvespectral efficiency of a communication system, in terms of, e.g.,bits/sec/Hz.

According to some aspects, β is used to process S7 antennacharacteristics in downlink. In order to do so, β must first beestimated. According to an aspect, this is done at the base stationusing information received in uplink, as will be further illustrated inFIGS. 6b and 6e . According to another aspect, β is estimated at thewireless device and then transmitted from the wireless device to thebase station for use in processing S7 antenna characteristics, as willbe further illustrated in FIGS. 6c -6 e.

FIG. 6b illustrates an aspect of the disclosed method where ChannelState Information, CSI, is processed at a base station, BS, usinginformation received in uplink. The base station comprises an antennaarray, wherein the antenna array has a plurality of antenna elements.Geometrical relationships between all pairs of antenna elements in aspatial layout of the antenna array have been obtained S1. All pairs ofantenna elements have been classified S3 into sets based on the obtainedgeometrical relationships, wherein all pairs of antenna elements in aset have substantially equal geometrical relationship in the spatiallayout. A representation of channel characteristics as P(β) has beendetermined S5, wherein argument β is a vector of elements, each elementrelating to a magnitude and/or phase of covariance between the antennaelements in the set, and P is a mapping function based on theclassifying S3.

According to an aspect, the wireless device transmits a reference signalin the uplink. According to a further aspect, the reference signal is aSounding Reference Signal, SRS, or a Demodulation Reference Signal,DMRS, per sub frame. The base station measures S71 the signalstransmitted from the wireless device in the uplink and elements of β areestimated based on the measurement. According to a further aspect, theestimation of β is then used for beamforming purposes, as is describedin relation to FIG. 6 below.

FIG. 6c illustrates an aspect of the disclosed method where CSI receivedin downlink is processed at a wireless device.

The base station comprises an antenna array, wherein the antenna arrayhas a plurality of antenna elements. Geometrical relationships betweenall pairs of antenna elements in a spatial layout of the antenna arrayhave been obtained Si. All pairs of antenna elements have beenclassified S3 into sets based on the obtained geometrical relationships,wherein all pairs of antenna elements in a set have substantially equalgeometrical relationship in the spatial layout. A representation ofchannel characteristics as P(β) has been determined S5, wherein argumentβ is a vector of elements, each element relating to a magnitude and/orphase of covariance between the antenna elements in the set, and P is amapping function based on the classifying S3.

For the wireless device to be able to estimate β, it must haveinformation about the representation P(β).

Turning back to the block diagram of FIG. 3, processing S7 antennacharacteristics comprises the base station transmitting S77 informationrelating to P to the wireless device, the information relating to Penabling estimation of β based on a measured signal.

According to an aspect, the information relating to P comprises therepresentation P(β).

According to a further aspect, P is a matrix defined by

vec{R_(mn)}=Pβ   (6)

wherein covariance between antenna elements is represented by acovariance matrix R_(mn), vec{R_(mn)} denoting the vectorization ofR_(mn), m and n being indices running over the number of antennaelements of the antenna array, and P is transmitted to the wirelessdevice.

According to another aspect, the representation P(β) is stored in thewireless device and the information relating to P, transmitted S77 tothe wireless device, comprises an indication relating to therepresentation P(β). According to an aspect, the indication is an indexand the wireless device selects the representation P(β) based on theindex.

According to an aspect, the wireless device performs S71 a measurementon signals received from the base station, as illustrated in FIG. 6c .According to a further aspect, the base station is part of a Long TermEvolution, LTE, network and transmits a plurality of CSI-RS, one perantenna. The wireless device estimates S72 elements of β based on themeasurement on the received signals.

FIG. 6d illustrates an aspect of the disclosed method where informationrelating to a vector β is transmitted to a base station, wherein thevector β relates to a representation P(β) determined according to thedisclosed method. According to an aspect, the information relating to βcomprises β.

FIG. 6e illustrates an aspect of the disclosed method where the antennacharacteristics of a base station, BS, is processed in a beamformingapplication in downlink. According to an aspect, processing S7 theantenna characteristics comprises performing S75 a signal processingoperation involving the covariance between antenna elements, wherein thesignal processing operation is expressed as a function of the elementsof β. According to an aspect, the signal processing operation is basedon a reference signal transmitted by a wireless device in the uplink, asillustrated in FIG. 6b . According to another aspect, the signalprocessing operation is based on information relating to β, theinformation relating to β being transmitted S74 to the base station froma wireless device, as illustrated in FIG. 6d . According to a furtheraspect, performing S75 the signal processing operation comprisesdetermining S76 at least one weight vector w based on the elements of β,the weight vector w consisting of antenna weights being used to adjustmagnitude and phase of signals to and from antenna elements of theantenna array.

According to an aspect, determining S76 at least one weight vector wcomprises evaluating the effect of the at least one weight vector w on achannel gain s, the channel gain being approximated by

s=E{|w^(H)h|²)   (7)

wherein E{} denotes the expectation value of the expression inside thecurly brackets, h is a channel vector and superscript H denotes aHermitian transpose. According to a further aspect, a covariance matrixR is defined as

R=E{hh^(H)}   (8).

According to a further aspect of the disclosure, the step of evaluatingthe effect of the at least one weight vector w on a channel gain srelates the channel gain s to the covariance matrix R by

s=w^(H)RW   (9)

wherein R is related to the representation P(β).

The expression in equation (9) can be written explicitly as a double sumof antenna element indices k, m, such that

$\begin{matrix}{s = {\sum\limits_{k}{\sum\limits_{m}{w_{k}^{H}R_{km}{w_{m}.}}}}} & (10)\end{matrix}$

wherein the number of unique elements of R will be determined by therepresentation P(β).

According to one aspect of the disclosure, the sums

$\begin{matrix}{\sum\limits_{u}{\sum\limits_{v}{w_{u}^{H}w_{v}}}} & (11)\end{matrix}$

are pre-calculated and stored in a vector wwU, where indices u and vcorrespond to replicates of the unique values of the covariance matrixR, the vector wwU having a length equal to the number of unique elementsof R. According to a further aspect, the sum of equation (10) isevaluated by

$\begin{matrix}{s = {\sum\limits_{n}\left( {{wwU}_{n} \cdot {Ru}_{n}} \right)}} & (12)\end{matrix}$

wherein Ru is a vector comprising the unique values of R.

According to an aspect, the channel gain is evaluated according toequation (7) for a uniform linear antenna comprising four antennaelements, whose covariance matrix R and corresponding representation Pand β are defined by equations (2), (4) and (5), respectively. Thevector Ru of equation (12) will then correspond to β of equation (5).FIG. 7 illustrates an aspect of error tolerance when determining arepresentation. According to an aspect of the disclosure, determining S5a representation comprises an error tolerance, such that

P(β)≈R   (13)

within the error tolerance, wherein ≈ denotes approximately equal to,and covariance between antenna elements is represented by a covariancematrix R.

FIG. 7a illustrates an antenna array comprising four antenna elements,wherein one antenna element is out of alignment with the other three.Using d to represent displacement vectors d_(mn), wherein thedisplacement vectors are obtained S1 as described in relation to FIGS. 4and 5, between nearest neighbour antenna pairs 1-2 and 2-3, that is d₁₂and d₂₃, the displacement vector d₃₄ can be written as {right arrow over(d)}+{right arrow over (δd)}, wherein {right arrow over (δd)} representan offset or error from having the geometrical relationship betweenantenna elements 3-4 to be {right arrow over (d)}.

FIG. 7b illustrates an aspect of the disclosed method, wherein therepresentation of channel characteristics comprises an error tolerance.According to an aspect, the error tolerance is represented by a norm||δd_(max)|| relating to a maximum norm of the difference between thegeometrical relationship reference {right arrow over (d)} and ageometrical relationship between a pair of antenna elements. Accordingto an aspect, the norm ||δd_(max)|| is used to classify S3 pairs ofantenna elements into sets, wherein all pairs of antenna elements in aset have substantially equal geometrical relationship in the spatiallayout if they do not differ more than the above-mentioned norm withrespect to a representative geometrical relationship. The representationP(β)≈R is determined S5 based on the sets obtained in the classificationS3 of pairs of antenna elements.

FIG. 8 illustrates an aspect of the method used for radar. The radarcomprises an antenna array, wherein the antenna array is a uniformlinear antenna array comprising four antenna elements. The four antennaelements are represented by black dots. The oval shape represents anobject upon which radar waves are reflected, wherein the radar waves arerepresented by arrows to and from the antenna elements. The antennaarray illustrated in FIG. 8 functions as both a transmit node and areceive node in a communication system. According to an aspect, handlingchannel characteristics comprises representing channel covariance with achannel covariance matrix defined by equation (2) and defining arepresentation according to equation (1), wherein P and β arerepresented by equations (4) and (5), respectively.

The radar arrangement illustrated in FIG. 8 is, according to someaspects, implemented in a vehicle, such as a car or a truck, where itforms part of a system for autonomous driving of the vehicle, and/or asystem for vehicle safety, such as a collision avoidance system.

According to some aspects it is desirable to not consider all possiblepairs when obtaining S1 geometrical relationships. According to someaspects it is desirable to adjust the number of sets in when classifyingS3 all pairs of antenna elements into sets. By doing so, physical andcomputational aspects may be taken into account when forming therepresentation P(β). FIGS. 9 and 10 demonstrate two embodiments, whichwhen compared to the aspect disclosed in FIG. 5, decreases andincreases, respectively, the number of parameters comprised in β.

FIG. 9 illustrates an aspect of the method for a uniform linear arraycomprising four antenna elements, in analogy with FIG. 5, but whereinthe representation comprises fewer unique elements.

FIG. 9a illustrates the antenna elements, represented as black dots, andFIGS. 9b and 9c illustrate geometrical relationships between antennaelements, the geometrical relationships being represented bydisplacement vectors.

FIGS. 9d-g illustrate sets comprising subsets of the geometricalrelationships of FIGS. 9b and c , each set comprising geometricalrelationships that have been classified to have substantially equalgeometrical relationship in the spatial layout.

FIG. 9h illustrates representative geometrical relationships, eachcorresponding to a respective set of FIGS. 9d-g . According to someaspects, it is desirable to represent one or more representativegeometrical relationships in terms of other geometrical relationships.

FIG. 9i illustrate one of the representative geometrical relationshipsof FIG. 9h being represented by another of the representativegeometrical relationships of FIG. 9h multiplied with a scaling factor α.The geometrical relationships between antenna elements 1 and 4 are thusrepresented by a scaling of a geometrical relationship representative ofthe geometrical relationships between next nearest neighbour pairs ofantenna elements.

FIG. 9j illustrates a set comprising the unique representativegeometrical relationships of FIG. 9i . Since the geometricalrelationship between antenna elements 1 and 4 of FIG. 9h can berepresented by a scaled geometrical relationship representative ofanother set of geometrical relationships, it is omitted.

FIG. 9k illustrates a representation of channel covariance, therepresentation comprising the representative geometrical relationshipsof FIG. 9j .

FIG. 9l illustrates a parametrisation of the representative geometricalrelationships of FIGS. 9i and j . Since one of the representativegeometrical relationships of FIG. 9h is described in terms of another,only five parameters, a-e, are needed.

FIG. 9m illustrates a covariance matrix based on the parametrisationillustrated in FIG. 9l of the representative geometrical relationshipsof FIGS. 9i and j , and the representation of channel covarianceillustrated in FIG. 9k . According to a further aspect of thedisclosure, β comprises the real valued parameters a, b, c, d and e.

FIG. 10 illustrates an aspect of the method for a uniform linear arraycomprising four antenna elements, in analogy with FIG. 5, but whereinthe representation comprises more unique elements.

FIG. 10a illustrates the antenna elements, represented as black dots,and FIGS. 10b and 10c illustrate geometrical relationships betweenantenna elements, the geometrical relationships being represented bydisplacement vectors.

FIGS. 10d-h illustrate sets comprising subsets of the geometricalrelationships of FIGS. 10b and c , each set comprising geometricalrelationships that have been classified to have substantially equalgeometrical relationship in the spatial layout, but wherein thegeometrical relationships between the outmost antenna elements have beenclassified S3 into separate sets (FIGS. 10g and h ).

FIG. 10i illustrates representative geometrical relationshipscorresponding to a respective set of FIGS. 10d -h.

FIG. 10j illustrates a set comprising the representative geometricalrelationships of FIG. 10i .

FIG. 10k illustrates a representation of channel covariance, wherein therepresentation comprises the representative geometrical relationships ofFIGS. 10i and j.

FIG. 10l illustrates a parametrisation of the representative geometricalrelationships of FIGS. 10i and j . Since the geometrical relationshipsbetween antenna elements 1 and 4 are classified into different sets, anextra representative geometrical relationship needs to be parametrised.The geometrical relationships are represented by real valued parametersa, b, c, d, e, f₁, f₂, g₁ and g₂, as illustrated in FIG. 10 l.

FIG. 10m illustrates a covariance matrix based on the parametrisationillustrated in FIG. 10l of the representative geometrical relationshipsof FIGS. 10i and j , and the representation of channel covarianceillustrated in FIG. 10k . According to an aspect of the disclosure, βcomprises the real valued parameters a-g₂, which is two parameters morethan if the antenna pairs relating to displacement vectors d₁₄ and d₄₁had been classified S3 to belong to the same set.

According to some aspects of the disclosure, the method is applied to aplurality of antenna arrays. The plurality of antenna arrays can be seenas one antenna system, for which an aspect of the present disclosure isapplied.

FIG. 11 illustrates an aspect of the method for two base stations.

FIG. 11a illustrates two base stations in a network, each base stationcomprising a uniform linear antenna array, wherein the respectiveantenna array comprises four antenna elements.

FIG. 11b illustrates representations of geometrical relationshipsbetween all pairs of antenna elements of the antenna arrays. BS1 r andBS2 r illustrate representations of geometrical relationships for basestations BS1 and BS2, respectively. BS1-BS2 r illustratesrepresentations of geometrical relationships for antenna pairs betweenthe two base stations.

Typically the distance between nodes of a network will be much greaterthan the spacing between antenna elements of an antenna in a node. Insuch cases, the representations of geometrical relationships illustratedin BS1-BS2 r will be nearly parallel.

FIG. 11c illustrates representations of two geometrical relationships.An increase in the distance between the base stations will decrease theangle between the representations of the two geometrical relationships.If the distance between the base stations is much greater than thespacing between antenna elements of the respective base stations, thesituation illustrated in FIG. 11d arises, where the two geometricalrelationships are substantially equal, to within an error 5d.

According to an aspect of the disclosure, a subset of the inter-antennapairs of antenna elements are classified S3 to belong to the same set,based on the obtained S1 geometrical relationships illustrated inBS1-BS2 r, wherein all pairs of antenna elements in the set havesubstantially equal geometrical relationship in the spatial layout.Thus, only a subset of the geometrical relationships illustrated inBS1-BS2 r will be considered unique.

According to a further aspect of the disclosure, all inter-antenna pairsof antenna elements are classified S3 to belong to the same set, basedon the obtained S1 geometrical relationships illustrated in BS1-BS2 r,wherein all pairs of antenna elements in the set have substantiallyequal geometrical relationship in the spatial layout. Thus, thegeometrical relationships illustrated in BS1-BS2 r can be represented bya single representative geometrical relationship.

FIG. 11e illustrates a covariance matrix according to an aspect. Theblocks R_BS1 to B2 and R_BS2 to B2 represent channel covariance betweenbase stations BS1 and BS2.

According to an aspect where only a subset of the geometricalrelationships illustrated in BS1-BS2 r will are classified as unique, aparametrization of covariance matrix elements of the blocks R_BS1 to B2and R_BS2 to B2 will result in fewer unique covariance matrix elementsthan if all inter-antenna channels were considered individually.

FIG. 12 illustrates a network node arranged to handle channelcharacteristics for an antenna array according to an aspect of thedisclosure. The network node 1100 comprises an antenna array 1101,wherein the antenna array 1101 has a plurality of antenna elements 1102.The network node 1100 further comprising processing means 1103 adaptedto

-   -   obtaining S1 geometrical relationships between any pair of        antenna elements 1102 in a spatial layout of the antenna array        1101,    -   classifying S3 all pairs of antenna elements 1102 into sets        based on the obtained geometrical relationships, wherein all        pairs of antenna elements 1102 in a set have substantially equal        geometrical relationship in the spatial layout,    -   determining S5 a representation of channel characteristics as        P(β), wherein argument β is a vector of elements, each element        relating to a magnitude and/or phase of covariance between the        antenna elements 1102 in the set, and P is a mapping function        based on the classifying S3, and    -   processing S7 antenna characteristics based on said        representation P(β).

According to an aspect, the processing means 1103 comprise a processor1104 and a memory 1105 wherein said memory 1105 is containinginstructions executable by said processor 1104.

The present disclosure also relates to modules for handling channelcharacteristics for an antenna array of a network node in acommunication system, according to an aspect of the disclosure, whereinthe antenna array has a plurality of antenna elements.

FIG. 13 illustrates modules for handling channel characteristics for anantenna array in a communication system, according to an aspect of thedisclosure. According to a further aspect, the modules are comprised ina terminal.

According to an aspect, one module is a geometrical relationshipobtaining module for obtaining S1 geometrical relationships between anypair of antenna elements in a spatial layout of the antenna array.

According to an aspect, one module is a classifying module forclassifying S3 all pairs of antenna elements into sets based on theobtained geometrical relationships, wherein all pairs of antennaelements in a set have substantially equal geometrical relationship inthe spatial layout,

According to an aspect, one module is a representation determiningmodule for determining S5 a representation of channel characteristics asP(β), wherein argument β is a vector of elements, each element relatingto a magnitude and/or phase of covariance between the antenna elementsin the set, and P is a mapping function based on the classifying S3.

According to an aspect, one module is a processing module for processing(S7) antenna characteristics based on said representation P(β).

The present disclosure also relates to a computer program for causing acomputer to handle channel characteristics for an antenna array in acommunication system, wherein the antenna array has a plurality ofantenna elements.

The computer program comprises computer readable code means which, whenrun on a computer, causes the computer to obtain S1 geometricalrelationships between any pair of antenna elements in a spatial layoutof the antenna array.

The computer program comprises computer readable code means which, whenrun on a computer, causes the computer to classify S3 all pairs ofantenna elements into sets based on the obtained geometricalrelationships, wherein all pairs of antenna elements in a set havesubstantially equal geometrical relationship in the spatial layout.

The computer program comprises computer readable code means which, whenrun on a computer, causes the computer to determining S5 arepresentation of channel characteristics as P(β), wherein argument β isa vector of elements, each element relating to a magnitude and/or phaseof covariance between the antenna elements in the set, and P is amapping function based on the classifying S3.

The computer program comprises computer readable code means which, whenrun on a computer, causes the computer to process S7 antennacharacteristics based on said representation P(β).

According to one aspect, the computer program comprises computerreadable code means for causing a computer to switch between at leasttwo different representations P(β).

With reference to FIG. 14, there is disclosed herein a method, performedin a wireless device O1, for channel characteristics handling for anantenna array A1 in a communication system, wherein the antenna arrayhas a plurality of antenna elements. the method comprises receiving SX1a mapping function P( ) based on a classifying of pairs of the antennaelements into sets, wherein all pairs of antenna elements in a set havesubstantially equal geometrical relationship in a spatial layout of theantenna elements in the antenna array, and processing SX3 channelcharacteristics related to the antenna array based on said mappingfunction P( ).

Hereby the wireless device benefits from a number of advantages. Forinstance, an advantage of this solution is that estimation of channelcharacteristics, by the wireless device, in the form of channelcovariance can be performed with similar quality, but lower complexitythan prior art methods. Another advantage is that Channel StateInformation, CSI, reporting, and storage of CSI reports, by the wirelessdevice, can be made more efficient. A further advantage is that thetraffic load of inter-node, such as an uplink or downlink between anetwork node and the wireless device is reduced. A yet further advantageis that memory requirements are reduced when it comes to storing channelcharacteristics. Another advantage is that signal processing operationsrelating to channel characteristics can be made more efficient in termsof implementation.

According to some aspects, the receiving SX1 further comprises receivingSX11 a representation of the channel characteristics as P(β), whereinargument β is a vector of elements, each element relating to a magnitudeand/or a phase of a covariance between antenna elements of the antennaarray.

Thus, by the present technique the full channel covariance matrix doesnot need to be communicated, but rather a representation of the channelcharacteristics as P(β). This allows for enhanced efficiency and reducedtraffic load in, e.g., uplink and downlink from and to the wirelessdevice.

According to some other aspects, the processing SX3 further comprisesperforming SX31 a measurement on at least one signal and estimating SX32elements of a vector β based on the performed measurement, each elementof the vector β relating to a magnitude and/or a phase of a covariancebetween antenna elements of the antenna array, wherein P(β) is arepresentation of the channel characteristics.

As mentioned above in discussions regarding the network nodes disclosedherein, signal processing operations for estimating elements of thevector β are according to some aspects equivalent to estimating the fullchannel covariance matrix, but may be implemented in a more efficientway by the wireless device. Consequently, according to some aspects, theprocessing SX3 comprises performing SX34 a signal processing operationinvolving a covariance between antenna elements of the antenna array,the signal processing operation being expressed as a function of theelements of β.

According to some further aspects, the processing SX3 is based on saidrepresentation P(β).

As mentioned above in discussions regarding the network nodes disclosedherein, communicating channel characteristics, and channel covariancematrices in particular, is more efficiently achieved through the presenttechnique. Thus, according to some aspects, the processing SX3 comprisestransmitting SX33 information relating to elements of a vector β, eachelement relating to a magnitude and/or a phase of a covariance betweenantenna elements of the antenna array, wherein P(β) is a representationof the channel characteristics.

According to some aspects, the mapping function P( ) is a matrix definedby

vec{R_(mn)}=Pβ

wherein covariance between antenna elements is represented by acovariance matrix R_(mn), vec{R_(mn)} denoting a vectorization ofR_(mn), m and n being indices running over the number of antennaelements of the antenna array.

There is also disclosed herein a wireless device O1 configured forchannel characteristics handling for an antenna array in a communicationsystem, wherein the antenna array has a plurality of antenna elements.

With reference to FIG. 15, the wireless device comprises a receivingmodule SY1 configured to receive a mapping function P( ) based on aclassifying of pairs of the antenna elements into sets, wherein allpairs of antenna elements in a set have substantially equal geometricalrelationship in a spatial layout of the antenna elements in the antennaarray, and a processing module SY3 configured to process channelcharacteristics related to the antenna array based on said mappingfunction P( )

According to some aspects, the receiving module SY1 is furtherconfigured to receive a representation of the channel characteristics asP(β), wherein argument β is a vector of elements, each element relatingto a magnitude and/or a phase of a covariance between antenna elementsof the antenna array.

According to some aspects, the processing module SY3 is furtherconfigured to perform a measurement on at least one signal and toestimate elements of a vector β based on the performed measurement, eachelement of the vector β relating to a magnitude and/or a phase of acovariance between antenna elements of the antenna array, wherein P(β)is a representation of the channel characteristics.

According to some aspects, the processing module SY3 is configured toprocess the channel characteristics based on said representation P(β).

According to some aspects, the processing module SY3 is configured totransmit information relating to elements of a vector β, each elementrelating to a magnitude and/or a phase of a covariance between antennaelements of the antenna array, wherein P(β) is a representation of thechannel characteristics.

According to some aspects, the processing module SY3 is furtherconfigured to perform a signal processing operation involving acovariance between antenna elements of the antenna array, the signalprocessing operation being expressed as a function of the elements of β.

The wireless device has already been discussed in connection to themethods performed in a wireless device above.

1. A method, performed in a network node, for channel characteristicshandling for an antenna array in a communication system, the antennaarray having a plurality of antenna elements, the method comprising:obtaining geometrical relationships between any pair of antenna elementsin a spatial layout of the antenna array; classifying all pairs ofantenna elements into sets based on the obtained geometricalrelationships, all pairs of antenna elements in a set havingsubstantially equal geometrical relationship in the spatial layout;determining a representation of channel characteristics as P(β),argument β being a vector of elements, each element relating to at leastone from the group consisting of a magnitude and phase of covariancebetween the antenna elements in the set, and P is a mapping functionbased on the classifying and processing antenna characteristics based onthe representation P(β).
 2. The method according to claim 1, wherein thestep of processing antenna characteristics comprises performing ameasurement on at least one signal and estimating elements of β based onthe measurement.
 3. The method according to claim 2, wherein the step ofperforming a measurement on at least one signal is based on a processingof at least one signal representing respective at least one pair of aset obtained in the classifying.
 4. The method according to claim 1,wherein the step of processing antenna characteristics comprisestransmitting information relating to β.
 5. The method according to claim4, wherein the step of transmitting information relating to β comprisestransmitting information relating to β between at least two networknodes.
 6. The method according to claim 4, wherein the step oftransmitting information relating to β comprises transmittinginformation relating to β to a memory.
 7. The method according to claim1, wherein the step of processing antenna characteristics comprisesperforming a signal processing operation involving the covariancebetween antenna elements, the signal processing operation beingexpressed as a function of the elements of β.
 8. The method according toclaim 7, wherein the step of performing a signal processing operationinvolving the covariance between antenna elements comprises determiningat least one weight vector w based on the elements of β, the weightvector w consisting of antenna weights being used to adjust magnitudeand/or phase of signals to and from antenna elements of the antennaarray.
 9. The method according to claim 1, wherein the step ofprocessing antenna characteristics comprises transmitting informationrelating to P, the information relating to P enabling estimation of βbased on a measured signal.
 10. The method according to claim 9, whereinthe information relating to P comprises the representation P(β).
 11. Themethod according to claim 9, wherein the information relating to Pcomprises an indication relating to a representation P(β) stored at areceiving node.
 12. The method according to claim 1, wherein the step ofdetermining a representation comprises an error tolerance, such thatP(β)≈R (1) within the error tolerance, wherein ≈ denotes approximatelyequal to, and covariance between antenna elements is represented by acovariance matrix R.
 13. The method according to claim 1, wherein P is amatrix defined byvec{R_(mn)}=Pβ (2) wherein covariance between antenna elements isrepresented by a covariance matrix R_(mn), vec{R_(mn)} denoting thevectorization of R_(mn), m and n being indices running over the numberof antenna elements of the antenna array.
 14. The method according toclaim 1, wherein the elements of β are real-valued.
 15. A network nodeconfigured to handle channel characteristics for an antenna array, thenetwork node comprising an antenna array, the antenna array having aplurality of antenna elements, the network node further comprisingprocessing means configured to: obtain geometrical relationships betweenany pair of antenna elements in a spatial layout of the antenna array;classify all pairs of antenna elements into sets based on the obtainedgeometrical relationships, all pairs of antenna elements in a set havingsubstantially equal geometrical relationship in the spatial layout;determine a representation of channel characteristics as P(β), argumentβ being a vector of elements, each element relating to at least one fromthe group consisting of a magnitude and phase of covariance between theantenna elements in the set, and P is a mapping function based on theclassifying; and process antenna characteristics based on therepresentation P(β).
 16. The network node according to claim 15, whereinthe processing means comprises a processor and a memory, wherein thememory contains instructions executable by the processor.
 17. (canceled)18. A method, performed in a wireless device for channel characteristicshandling for an antenna array in a communication system, the antennaarray having a plurality of antenna elements, the method comprising:receiving a mapping function P( ) based on a classifying of pairs of theantenna elements into sets, all pairs of antenna elements in a sethaving substantially equal geometrical relationship in a spatial layoutof the antenna elements in the antenna array; and processing channelcharacteristics related to the antenna array based on the mappingfunction P( ).
 19. The method according to claim 18, wherein thereceiving further comprises receiving a representation of the channelcharacteristics as P(β), wherein argument β is a vector of elements,each element relating to at least one from the group consisting of amagnitude and phase of a covariance between antenna elements of theantenna array.
 20. The method according to claim 18, wherein theprocessing further comprises performing a measurement on at least onesignal and estimating elements of a vector β based on the performedmeasurement, each element of the vector β relating to at least one fromthe group consisting of a magnitude and phase of a covariance betweenantenna elements of the antenna array, wherein P(β) is a representationof the channel characteristics.
 21. The method according to claim 19,wherein the processing is based on the representation P(β).
 22. Themethod according to claim 18, wherein the processing comprisestransmitting information relating to elements of a vector β each elementrelating to at least one from the group consisting of a magnitude andphase of a covariance between antenna elements of the antenna array,wherein P(β) is a representation of the channel characteristics.
 23. Themethod according to claim 19, wherein the processing comprisesperforming a signal processing operation involving a covariance betweenantenna elements of the antenna array, the signal processing operationbeing expressed as a function of the elements of β.
 24. The methodaccording to any of claim 19, wherein the mapping function P( ) is amatrix defined byvec{R_(mn)}=Pβ wherein covariance between antenna elements isrepresented by a covariance matrix R_(mn), vec{R_(mn)} denoting avectorization of R_(mn), m and n being indices running over the numberof antenna elements of the antenna array.
 25. (canceled)
 26. A wirelessdevice configured for channel characteristics handling for an antennaarray in a communication system, the antenna array having a plurality ofantenna elements, the wireless device comprising: a receiving moduleconfigured to receive a mapping function P( ) based on a classifying ofpairs of the antenna elements into sets, all pairs of antenna elementsin a set having substantially equal geometrical relationship in aspatial layout of the antenna elements in the antenna array; and aprocessing module configured to process channel characteristics relatedto the antenna array based on the mapping function P( ).
 27. Thewireless device according to claim 26, wherein the receiving module isfurther configured to receive a representation of the channelcharacteristics as P(β), wherein argument β is a vector of elements,each element relating to at least one from the group consisting of amagnitude and phase of a covariance between antenna elements of theantenna array.
 28. The wireless device according to claim 26, whereinthe processing module is further configured to perform a measurement onat least one signal and to estimate elements of a vector β based on theperformed measurement, each element of the vector β relating to at leastone from the group consisting of a magnitude and phase of a covariancebetween antenna elements of the antenna array, wherein P(β) is arepresentation of the channel characteristics.
 29. The wireless deviceaccording to claim 27, wherein the processing module is configured toprocess the channel characteristics based on said representation P(β).30. The wireless device according to claim 26, wherein the processingmodule is configured to transmit information relating to elements of avector β, each element relating to at least one from the groupconsisting of a magnitude and phase of a covariance between antennaelements of the antenna array, wherein P(β) is a representation of thechannel characteristics.
 31. The wireless device according to claim 27,wherein the processing module is further configured to perform a signalprocessing operation involving a covariance between antenna elements ofthe antenna array, the signal processing operation being expressed as afunction of the elements of β.